FOCAL: Filtered On-device Continuous Activity Logging for Efficient Personal Desktop Summarization
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arXiv
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| Format: | Preprint |
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2026
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| _version_ | 1866918459716141056 |
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| author | Yin, Haoran Wen, Zhiyuan Cao, Jiannong Yuan, Bo Yang, Ruosong |
| author_facet | Yin, Haoran Wen, Zhiyuan Cao, Jiannong Yuan, Bo Yang, Ruosong |
| contents | Desktop interaction streams provide a continuous, privacy-sensitive record of interleaved user tasks. Transforming these streams into task-organized personal logs on-device faces two main challenges: exhaustive Vision-Language Model (VLM) processing strains local resources, and global stream processing causes cross-task context pollution. We present FOCAL (Filtered On-device Continuous Activity Logging), a privacy-first multi-agent system utilizing a unified filter-plan-log architecture. It cascades a lightweight Filter Agent for noise suppression, a text-only Brain Agent for task attribution, a Record Agent for selective visual reasoning, and a task-isolated Memory Agent for context-coherent summarization. Experiments on DesktopBench (comprising 2,572 screenshots across 420 complex sessions) show FOCAL reduces total token consumption by 60.4% and VLM call count by 72.3% versus a baseline, while boosting Key Information Recall (KIR) from 0.38 to 0.61. Crucially, under $A{\to}B{\to}A$ task interruptions, FOCAL maintains Task Acc 0.81 and KIR 0.80, whereas the baseline collapses to Task Acc 0.03. FOCAL pioneers the efficient, on-device summarization of instruction-free desktop streams into multi-perspective personal logs. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_19541 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | FOCAL: Filtered On-device Continuous Activity Logging for Efficient Personal Desktop Summarization Yin, Haoran Wen, Zhiyuan Cao, Jiannong Yuan, Bo Yang, Ruosong Multiagent Systems Human-Computer Interaction Desktop interaction streams provide a continuous, privacy-sensitive record of interleaved user tasks. Transforming these streams into task-organized personal logs on-device faces two main challenges: exhaustive Vision-Language Model (VLM) processing strains local resources, and global stream processing causes cross-task context pollution. We present FOCAL (Filtered On-device Continuous Activity Logging), a privacy-first multi-agent system utilizing a unified filter-plan-log architecture. It cascades a lightweight Filter Agent for noise suppression, a text-only Brain Agent for task attribution, a Record Agent for selective visual reasoning, and a task-isolated Memory Agent for context-coherent summarization. Experiments on DesktopBench (comprising 2,572 screenshots across 420 complex sessions) show FOCAL reduces total token consumption by 60.4% and VLM call count by 72.3% versus a baseline, while boosting Key Information Recall (KIR) from 0.38 to 0.61. Crucially, under $A{\to}B{\to}A$ task interruptions, FOCAL maintains Task Acc 0.81 and KIR 0.80, whereas the baseline collapses to Task Acc 0.03. FOCAL pioneers the efficient, on-device summarization of instruction-free desktop streams into multi-perspective personal logs. |
| title | FOCAL: Filtered On-device Continuous Activity Logging for Efficient Personal Desktop Summarization |
| topic | Multiagent Systems Human-Computer Interaction |
| url | https://arxiv.org/abs/2604.19541 |